Title: How to Download a Trained Model from fast.ai and Deploy it on Heroku
Fast.ai is a powerful deep learning library that makes it easy to build and train state-of-the-art machine learning models. With its user-friendly interface and excellent documentation, fast.ai has gained popularity among data scientists and machine learning enthusiasts.
In this article, we will discuss the process of downloading a trained model from fast.ai and deploying it on Heroku. Heroku is a cloud platform that allows developers to build, deliver, monitor, and scale applications quickly and easily.
Step 1: Train a Model using fast.ai
Before you can deploy a trained model on Heroku, you need to create and train a model using the fast.ai library. Fast.ai provides various tutorials and courses to help you get started, so make sure to familiarize yourself with the library and train a model using your dataset.
Step 2: Save the Trained Model
Once you have trained a model using fast.ai, you need to save the trained model along with its architecture and weights. Fast.ai provides a function called `export` that allows you to export your trained model to a file. For example, if you have trained a vision model using fast.ai, you can use the following code to export the model:
“`python
learn.export(‘vision_model.pkl’)
“`
Step 3: Create a Flask Application
To deploy the trained model on Heroku, you need to create a web application that can load the model and make predictions. You can use the Flask web framework to create a simple web application that serves the trained model. Here’s an example of a Flask application that loads the trained model and makes predictions:
“`python
from flask import Flask, request, jsonify
from fastai.vision import load_learner
app = Flask(__name__)
model = load_learner(‘path_to_your_exported_model.pkl’)
@app.route(‘/predict’, methods=[‘POST’])
def predict():
data = request.get_json()
prediction = model.predict(data[‘image’])
return jsonify({‘prediction’: str(prediction[0])})
if __name__ == ‘__main__’:
app.run(debug=True)
“`
Step 4: Deploy the Application on Heroku
Once you have created the Flask application, you can deploy it on Heroku. First, create a new Heroku app and push your code to the Heroku app repository using Git. You can then use the Heroku CLI to deploy your application with the following commands:
“`bash
heroku create
git push heroku master
heroku ps:scale web=1
“`
After deploying the application, you can access the prediction endpoint using the URL provided by Heroku. You can then use the endpoint to make predictions using the trained model.
In conclusion, deploying a trained model from fast.ai on Heroku is a straightforward process that involves saving the trained model, creating a Flask application, and deploying the application on Heroku. With the power of fast.ai and the ease of Heroku’s platform, you can quickly deploy and scale your machine learning models for real-world applications.
